Predict domains likely to mediate interaction and if they tend to bind linear motifs (the domains predicted are enriched in domains known to bind linear motifs)

Setting up how many cores to use for analysis and fold-enrichment vs freqency

# cores to use for multiprocessing: is the number of cores is small -> we are not on a cluster -> use all; if the number of cores is large -> we are on cluster -> use only 15 or 7 or 31 cores (n requested-1)
if(detectCores() <= 4) cores_to_use = detectCores() - 1
if(detectCores() > 4) cores_to_use = 6
cores_to_use = NULL

# how many permutations?
N_permut = 1000

filename = paste0("./processed_data_files/predict_domain_human_clust",gsub("-","",Sys.Date()),".RData")
filename = paste0("./processed_data_files/predict_domain_human_clust",gsub("-","","2018-08-19"),".RData")

How many of the domains identified using our approach are in the ELM database (as compared to the population of all domains we tested)?

Calculate empirical pvalues using count of a domain among human interacting partners of each human protein

file_all = "./processed_data_files/human_net_w_domains"
gunzip(paste0(file_all,".gz"), remove = F)
data_all = fread(file_all, sep = "\t", stringsAsFactors = F)
data_all = data_all[IDs_interactor_human_B != "UNKNOWN"]
unlink(file_all)

file_BioPlex3 = "./processed_data_files/BioPlex3human_net_w_domains"
gunzip(paste0(file_BioPlex3,".gz"), remove = F)
data_BioPlex3 = fread(file_BioPlex3, sep = "\t", stringsAsFactors = F)
data_BioPlex3 = data_BioPlex3[IDs_interactor_human_B != "UNKNOWN"]
unlink(file_BioPlex3)

# count function: set up standard parameters
permuteCount = function(data, select_nodes = NULL, also_permuteYZ = F, N_permut = 10, clustermq_jobs = 60, clustermq_mem = 12500){
    permutationPval(interactions2permute = IDs_interactor_human_A ~ IDs_interactor_human_B, # first set of interacting pairs (XY) that are to be permuted
                    associations2test = IDs_interactor_human_A ~ IDs_domain_human_B, # set of interacting pairs to be tested (XZ), YZ interactions are assumed
                    node_attr = list(IDs_interactor_human_A ~ IDs_interactor_human_A_degree,
                                     IDs_domain_human_B ~ domain_count,
                                     IDs_interactor_human_A + IDs_domain_human_B ~ domain_frequency_per_IDs_interactor_human_A),
                    data = data,
                    statistic = IDs_interactor_human_A + IDs_domain_human_B ~ .N,
                    select_nodes = select_nodes,
                    N = N_permut,
                    cores = NULL, seed = 2, also_permuteYZ = F,
                    clustermq = T, clustermq_mem = clustermq_mem,
                    split_comp_inner_N = 4, clustermq_jobs = clustermq_jobs,
                    clustermq_log_worker = F)
}
# count: all protein interactions in IntAct and all domains - # permute IDs_interactor_human_A ~ IDs_interactor_human_B
if(file.exists(filename)) load(filename)
if(!"res_count_all" %in% objects()){
    time = proc.time()
    res_count_all = permuteCount(data_all, select_nodes = IDs_domain_human_B ~ domain_count >= 1,
                                 N_permut = 5000, clustermq_jobs = 100, clustermq_mem = 12500)
    proc.time() - time
    # save results
    save(list = ls(), file=filename)
}

## Submitting 100 worker jobs (ID: 7307) ...

## Running 1,250 calculations (1 calls/chunk) ...

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## Warning in summarize_result(job_result, n_errors, n_warnings, cond_msgs, : 100 warnings occurred in processing
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## Master: [16444.0s 8.5% CPU]; Worker: [avg 97.3% CPU, max 11958.6 Mb]

plot(res_count_all, main = "count: all proteins and domains")

# count: protein interactions in BioPlex3 and all domains - # permute IDs_interactor_human_A ~ IDs_interactor_human_B
if(!"res_count_BioPlex3" %in% objects()){
    time = proc.time()
    res_count_BioPlex3 = permuteCount(data_BioPlex3, select_nodes = IDs_domain_human_B ~ domain_count >= 1,
                                      N_permut = 10000, clustermq_jobs = 100, clustermq_mem = 2500)
    proc.time() - time
    # save results
    save(list = ls(), file=filename)
}

## Submitting 100 worker jobs (ID: 6574) ...

## Running 2,500 calculations (2 calls/chunk) ...

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## Warning in master(qsys = qsys, iter = df, rettype = rettype, fail_on_error
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## Warning in master(qsys = qsys, iter = df, rettype = rettype, fail_on_error
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## Warning in master(qsys = qsys, iter = df, rettype = rettype, fail_on_error
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## Warning in master(qsys = qsys, iter = df, rettype = rettype, fail_on_error
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## Warning in master(qsys = qsys, iter = df, rettype = rettype, fail_on_error
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## Warning in master(qsys = qsys, iter = df, rettype = rettype, fail_on_error
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## Warning in master(qsys = qsys, iter = df, rettype = rettype, fail_on_error
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## Warning in master(qsys = qsys, iter = df, rettype = rettype, fail_on_error
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## Warning in master(qsys = qsys, iter = df, rettype = rettype, fail_on_error
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## Warning in master(qsys = qsys, iter = df, rettype = rettype, fail_on_error
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## Warning in master(qsys = qsys, iter = df, rettype = rettype, fail_on_error
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## Master: [2336.9s 9.1% CPU]; Worker: [avg 97.6% CPU, max 1734.2 Mb]

plot(res_count_BioPlex3, main = "count: all proteins and domains (BioPlex)")

Human protein degree and the background domain count of top-scoring proteins

# count
PermutResult2D(res = res_count_all, N = 250) +
    ggtitle("2D-bin plots of 250 top-scoring human protein - domain pairs, \n statistic: count of a domain among interacting partners of a protein (IntAct)")

# Fisher test p-value
PermutResult2D(res = res_count_BioPlex3, N = 250) + 
    ggtitle("2D-bin plots of 250 top-scoring human protein - domain pairs, \n statistic: count of a domain among interacting partners of a protein (BioPlex3)")

Map domains known to interact with linear motifs from ELM to the domains we found

if(!file.exists("./data_files/interactiondomains.tsv")) downloader::download("http://elm.eu.org/interactiondomains.tsv","./data_files/interactiondomains.tsv")
interactiondomains = fread("./data_files/interactiondomains.tsv")
interactiondomains[, pfam_id := `Interaction Domain Id`]

domains_known = interactiondomains[, unique(pfam_id)]

InterProScan_domains = readInterProGFF3("../viral_project/processed_data_files/all_human_viral_protein_domains.gff3.gz")
# get InterProID to member database ID mapping
InterPro2memberDB = getInterPro2memberDB(InterProScan_domains)
InterPro2memberDB = InterPro2memberDB[complete.cases(InterPro2memberDB)]
domains_known_mapped = unique(InterPro2memberDB[memberDBID %in% domains_known | InterProID %in% domains_known, InterProID])
domains_not_mapped = unique(domains_known[!(domains_known %in% InterPro2memberDB$memberDBID | domains_known %in% InterPro2memberDB$InterProID)])

I did Fisher test to evaluate if the domains that we find are enriched in domains known to interact with linear motifs (from ELM). I have picked most likely human protein - human domain associations (by p-value). Then I counted how many known motif-binding domains we have found and did the Fisher test. Finally, I was choosing different p-value cutoffs.

testEnrichment = function(N, res, rank_by = "p.value", domains_known_mapped, random = F, name = "", decreasing = F){
    if(random) {
        res$data_pval = unique(res$data_with_pval[,c("IDs_interactor_human_A", "IDs_domain_human_B", rank_by, "domain_type", "domain_count", "IDs_interactor_human_A_degree"), with = F])
        domains_found = res$data_pval[sample(1:nrow(res$data_with_pval), N), unique(IDs_domain_human_B)]
    } else {
        res$data_pval = unique(res$data_with_pval[,c("IDs_interactor_human_A", "IDs_domain_human_B", rank_by, "domain_type", "domain_count", "IDs_interactor_human_A_degree"), with = F])
        ind = order(unlist(res$data_pval[, c(rank_by), with = F]), decreasing = decreasing)[1:N] 
        domains_found = res$data_pval[ind, unique(IDs_domain_human_B)]
    }
    
    alldomains = res$data_with_pval[, unique(IDs_domain_human_B)]
    known = factor(alldomains %in% domains_known_mapped, levels = c("TRUE", "FALSE"))
    found = factor(alldomains %in% domains_found, levels = c("TRUE", "FALSE"))
    table_res = table(known, found)
    
    test = fisher.test(table(known, found), alternative = "greater", conf.int = T)
    
    return(c(pval = test$p.value, odds_ratio = as.vector(test$estimate),
             count = table_res["TRUE", "TRUE"], total_count = sum(as.logical(found)),
             name = name))
}
runningTestEnrichment = function(res, name, rank_by = "p.value", decreasing = F, Ns){
    enrichment = sapply(Ns, testEnrichment, res = res, 
                        domains_known_mapped = domains_known_mapped, 
                        name = name, rank_by = rank_by, decreasing = decreasing)
    colnames(enrichment) = Ns
    return(enrichment)
}

Ns = seq(25, 1000, 40)
# count
enrichment = runningTestEnrichment(res_count_all, name = "domain frequency among interactors of a human protein, empirical pval", Ns = Ns)
enrichment_justfreq = runningTestEnrichment(res_count_all, rank_by = "observed_statistic", name = "domain count among interactors of a human protein, frequency", Ns = Ns) 
enrichmentBioPlex3 = runningTestEnrichment(res_count_BioPlex3, name = "domain frequency among interactors of a human protein (BioPlex3), empirical pval", Ns = Ns)

random_domains = function(N = 100, seed = seed, Ns = seq(25, 1000, 40), res){
    set.seed(seed)
    
    quantiles = c(0.975, 0.75, 0.5, 0.25, 0.025)
    quantile_names = c("97.5% quantile", "75% quantile", "median", "25% quantile", "2.5% quantile")
    
    all_temp = Q(fun = function(i) {
        library(data.table)
        enrichmentRANDOM = sapply(Ns, testEnrichment, res = res, domains_known_mapped = domains_known_mapped, random = T, name = "N random proteins")[1:3,]
        rownames = rownames(enrichmentRANDOM)
        enrichmentRANDOM = matrix(as.numeric(unlist(enrichmentRANDOM)),nr=nrow(enrichmentRANDOM))
        colnames(enrichmentRANDOM) = Ns
        rownames(enrichmentRANDOM) = rownames
        enrichmentRANDOM
    }, 1:N,
    export = list(Ns = Ns, testEnrichment = testEnrichment,
                  res = res, domains_known_mapped = domains_known_mapped),
    seed = seed, memory = 1000, n_jobs = 20, rettype = "list")
    
    #pval_temp = replicate(N, {
    #    enrichmentRANDOM = sapply(Ns, testEnrichment, res = res, domains_known_mapped = domains_known_mapped, random = T, name = "N random proteins")[1,]
    #    names(enrichmentRANDOM) = Ns
    #    as.numeric(enrichmentRANDOM)
    #})
    pval_temp = sapply(all_temp, function(m) m[1,])
    pval = apply(pval_temp, 1, quantile, probs = quantiles)
    rownames(pval) = quantile_names
    colnames(pval) = Ns
    
    #odds_ratio_temp = replicate(N, {
    #    enrichmentRANDOM = sapply(Ns, testEnrichment, res = res, domains_known_mapped = domains_known_mapped, random = T, name = "N random proteins")[2,]
    #    names(enrichmentRANDOM) = Ns
    #    as.numeric(enrichmentRANDOM)
    #})
    odds_ratio_temp = sapply(all_temp, function(m) m[2,])
    odds_ratio = apply(odds_ratio_temp, 1, quantile, probs = quantiles)
    rownames(odds_ratio) = quantile_names
    colnames(odds_ratio) = Ns
    
    #count_temp = replicate(N, {
    #    enrichmentRANDOM = sapply(Ns, testEnrichment, res = res, domains_known_mapped = domains_known_mapped, random = T, name = "N random proteins")[3,]
    #    names(enrichmentRANDOM) = Ns
    #    as.numeric(enrichmentRANDOM)
    #})
    count_temp = sapply(all_temp, function(m) m[3,])
    count = apply(count_temp, 1, quantile, probs = quantiles)
    rownames(count) = quantile_names
    colnames(count) = Ns
    
    return(list(pval = pval, odds_ratio = odds_ratio, count = count))
}

if(!"enrichmentRANDOM" %in% objects()){
    enrichmentRANDOM = random_domains(1000, 1, res = res_count_all)
    # save results
    save(list = ls(), file=filename)
}

## Submitting 20 worker jobs (ID: 6531) ...

## Running 1,000 calculations (1 calls/chunk) ...

## Master: [430.4s 1.4% CPU]; Worker: [avg 99.3% CPU, max 566.2 Mb]

As we include more proteins, the number of known domains we find increases and then levels off (probably because some of the known SLIM-binding domains do not interact with viral proteins). ????? is this true for human? should always increase

plotEnrichment(enrichment, enrichment_justfreq, enrichmentBioPlex3,
               random_domains = enrichmentRANDOM, 
               domains_known_mapped = domains_known_mapped, type = "count", plot_type = "l")

As we include more proteins, the Fisher test odds ratio decreases (we add more stuff that is not known). Odds ratio measures how much more likely are we to find a domain using our procedure if it’s a known domain as compared to if it’s not a known domain.

plotEnrichment(enrichment, enrichment_justfreq, enrichmentBioPlex3,
               random_domains = enrichmentRANDOM, 
               domains_known_mapped = domains_known_mapped, type = "odds_ratio", plot_type = "l")

corresponding P-values from the Fisher test

plotEnrichment(enrichment, enrichment_justfreq, enrichmentBioPlex3,
               random_domains = enrichmentRANDOM, 
               domains_known_mapped = domains_known_mapped, type = "pval", plot_type = "l")

How many human proteins are known per each of the domain instances in top 200 protein-domain pairs?

selectTopHits = function(res, N){
    res$data_pval = unique(res$data_with_pval[,.(IDs_interactor_human_A, IDs_domain_human_B, p.value, domain_type, domain_count, IDs_interactor_human_A_degree)])
    pairs200pval = res$data_pval[order(p.value, decreasing = F)[1:N], max(p.value)]
    
    restop100 = res
    restop100$data_with_pval = restop100$data_with_pval[p.value <= pairs200pval, ]
    restop100$data_with_pval[, N_human_per_human_w_domain := length(unique(IDs_interactor_human_A)), by = .(IDs_interactor_human_B, IDs_domain_human_B)]
    return(restop100)
}
restop100 = selectTopHits(res_count_all, N = 250)
plot(restop100, IDs_interactor_human_B ~ N_human_per_human_w_domain)

plot(restop100)

1009 human proteins with enriched domains have 5 or more human interacting partners.
421 human proteins with enriched domains have 10 or more human interacting partners.

what are those domains? are they known ELM-interacting domains? which proteins they are in?

restop100$data_with_pval[N_human_per_human_w_domain >= 10, unique(IDs_domain_human_B)]

##  [1] "IPR013087" "IPR000608" "IPR016135" "IPR027417" "IPR001766"
##  [6] "IPR011991" "IPR000980" "IPR001452" "IPR001811" "IPR000719"
## [11] "IPR011009" "IPR013083" "IPR000504" "IPR001841" "IPR011993"
## [16] "IPR003309" "IPR008916" "IPR032444" "IPR029071" "IPR001245"
## [21] "IPR020635" "IPR033899" "IPR004827" "IPR013783" "IPR027409"
## [26] "IPR027413" "IPR023410" "IPR007110" "IPR017986" "IPR015943"

restop100$data_with_pval[N_human_per_human_w_domain >= 10, unique(IDs_domain_human_B)] %in% domains_known_mapped

##  [1] FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE
## [12] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
## [23] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE

restop100$data_with_pval[N_human_per_human_w_domain >= 10 & IDs_domain_human_B == "IPR000504", unique(IDs_interactor_human_B)]

##  [1] "O14979" "O43390" "O60506" "O75494" "O95758" "P07910" "P08621"
##  [8] "P09012" "P09651" "P11940" "P14866" "P19338" "P22626" "P23246"
## [15] "P26368" "P26599" "P31483" "P31942" "P31943" "P35637" "P38159"
## [22] "P43243" "P51991" "P52272" "P52597" "P55795" "P62995" "P84103"
## [29] "P98175" "P98179" "Q01081" "Q01130" "Q01844" "Q07955" "Q08170"
## [36] "Q13151" "Q13242" "Q13243" "Q13247" "Q13310" "Q13595" "Q14011"
## [43] "Q14103" "Q14498" "Q15233" "Q15287" "Q15415" "Q15717" "Q16629"
## [50] "Q16630" "Q86V81" "Q96PK6" "Q99729" "Q9BWF3" "Q9NR30" "Q9NZI8"
## [57] "Q9UBU9" "Q9UHX1" "Q9UKM9" "Q9UKV3" "Q9Y5S9"

DT::datatable(restop100$data_with_pval[N_human_per_human_w_domain >= 10,])

## Warning in instance$preRenderHook(instance): It seems your data is too
## big for client-side DataTables. You may consider server-side processing:
## https://rstudio.github.io/DT/server.html

R session information

save(list = ls(), file=filename)
#R.utils::gzip(filename = "./processed_data_files/predict_domain_human_clust11092017.RData",
#              destname = "./processed_data_files/predict_domain_human_clust11092017.RData.gz",
#              remove = T, overwrite = T)
Sys.Date()

## [1] "2018-08-18"

sessionInfo()

## R version 3.4.4 (2018-03-15)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Red Hat Enterprise Linux Server 7.3 (Maipo)
## 
## Matrix products: default
## BLAS: /nfs/research1/petsalaki/users/vitalii/R/lib64/R/lib/libRblas.so
## LAPACK: /nfs/research1/petsalaki/users/vitalii/R/lib64/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
##  [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
##  [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats4    parallel  methods   stats     graphics  grDevices utils    
## [8] datasets  base     
## 
## other attached packages:
##  [1] clustermq_0.8.4.1    R.utils_2.6.0        R.oo_1.22.0         
##  [4] R.methodsS3_1.7.1    RColorBrewer_1.1-2   GGally_1.4.0        
##  [7] ggplot2_3.0.0        rtracklayer_1.38.3   GenomicRanges_1.30.3
## [10] GenomeInfoDb_1.14.0  MItools_0.1.38       Biostrings_2.46.0   
## [13] XVector_0.18.0       data.table_1.11.4    PSICQUIC_1.16.4     
## [16] plyr_1.8.4           httr_1.3.1           biomaRt_2.34.2      
## [19] IRanges_2.12.0       S4Vectors_0.16.0     BiocGenerics_0.24.0 
## [22] rmarkdown_1.10      
## 
## loaded via a namespace (and not attached):
##  [1] bitops_1.0-6               matrixStats_0.54.0        
##  [3] bit64_0.9-7                progress_1.2.0            
##  [5] rprojroot_1.3-2            tools_3.4.4               
##  [7] backports_1.1.2            R6_2.2.2                  
##  [9] DT_0.4                     KernSmooth_2.23-15        
## [11] DBI_1.0.0                  lazyeval_0.2.1            
## [13] colorspace_1.3-2           withr_2.1.2               
## [15] tidyselect_0.2.4           prettyunits_1.0.2         
## [17] ontologyIndex_2.4          bit_1.1-14                
## [19] compiler_3.4.4             Biobase_2.38.0            
## [21] DelayedArray_0.4.1         caTools_1.17.1.1          
## [23] scales_0.5.0               stringr_1.3.1             
## [25] digest_0.6.15              Rsamtools_1.30.0          
## [27] pkgconfig_2.0.1            htmltools_0.3.6           
## [29] htmlwidgets_1.2            rlang_0.2.1               
## [31] RSQLite_2.1.1              shiny_1.1.0               
## [33] bindr_0.1.1                jsonlite_1.5              
## [35] crosstalk_1.0.0            BiocParallel_1.12.0       
## [37] gtools_3.8.1               dplyr_0.7.6               
## [39] RCurl_1.95-4.11            magrittr_1.5              
## [41] GenomeInfoDbData_1.0.0     Matrix_1.2-14             
## [43] Rcpp_0.12.18               munsell_0.5.0             
## [45] proto_1.0.0                stringi_1.2.4             
## [47] yaml_2.2.0                 SummarizedExperiment_1.8.1
## [49] zlibbioc_1.24.0            gplots_3.0.1              
## [51] qvalue_2.10.0              grid_3.4.4                
## [53] blob_1.1.1                 promises_1.0.1            
## [55] gdata_2.18.0               crayon_1.3.4              
## [57] lattice_0.20-35            splines_3.4.4             
## [59] hms_0.4.2                  knitr_1.20                
## [61] pillar_1.3.0               reshape2_1.4.3            
## [63] infuser_0.2.8              XML_3.98-1.13             
## [65] glue_1.3.0                 evaluate_0.11             
## [67] downloader_0.4             httpuv_1.4.5              
## [69] gtable_0.2.0               purrr_0.2.5               
## [71] reshape_0.8.7              assertthat_0.2.0          
## [73] gsubfn_0.7                 mime_0.5                  
## [75] xtable_1.8-2               later_0.7.3               
## [77] tibble_1.4.2               GenomicAlignments_1.14.2  
## [79] AnnotationDbi_1.40.0       memoise_1.1.0             
## [81] bindrcpp_0.2.2             rzmq_0.9.3                
## [83] ROCR_1.0-7